– Shortlist and identify data sources for segmentation and profiling such as from internal sources – transaction data, customer master data (demographic data), product data, product master data and external sources – Credit data
– Establish criteria to select customers whose data needs to be pulled for analysis.
– Explore data and identify data quality gaps, trends, data variance and cleanse & transform the data.
– Develop unsupervised learning models for customer segmentation from behavioral data and tune for optimum clusters.
– Discover customer profiles from demographic customer data
– Communicate the data exploration and model results to business analyst and gather the feedback.
– Provide inputs for Dashboard Design
– Coordinate in dashboard testing
– Perform documentation of the developed solution
– Strong problem solving skills.
– Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
– Experience working with and creating data architectures.
– Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
– Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
– Excellent written and verbal communication skills for coordinating across teams.